Overview

Dataset statistics

Number of variables20
Number of observations23086
Missing cells8422
Missing cells (%)1.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.4 MiB
Average record size in memory426.8 B

Variable types

Categorical7
DateTime3
Numeric8
Text2

Alerts

Tipo de registro del contacto has constant value ""Constant
RFM Segmento Actual has constant value ""Constant
Donante Activo has constant value ""Constant
Cantidad Cuotas Pagadas Global is highly overall correlated with Edad_donacionHigh correlation
Edad_donacion is highly overall correlated with Cantidad Cuotas Pagadas GlobalHigh correlation
Otra Clasificación RFM Actual is highly imbalanced (63.2%)Imbalance
Género has 400 (1.7%) missing valuesMissing
Estado Civil has 2428 (10.5%) missing valuesMissing
Ocupación has 4672 (20.2%) missing valuesMissing
Monto Actual is highly skewed (γ1 = 58.93183823)Skewed
Cantidad de Hijos has 7285 (31.6%) zerosZeros
Lapsed Probability has 20060 (86.9%) zerosZeros
Cantidad Cuotas No Pagadas Global has 9269 (40.1%) zerosZeros
Edad_donacion has 939 (4.1%) zerosZeros

Reproduction

Analysis started2023-09-04 23:35:57.507086
Analysis finished2023-09-04 23:36:02.797843
Duration5.29 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Tipo de registro del contacto
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size203.1 KiB
Donante
23086 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters161602
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDonante
2nd rowDonante
3rd rowDonante
4th rowDonante
5th rowDonante

Common Values

ValueCountFrequency (%)
Donante 23086
100.0%

Length

2023-09-04T18:36:02.828756image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-04T18:36:02.883560image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
donante 23086
100.0%

Most occurring characters

ValueCountFrequency (%)
n 46172
28.6%
D 23086
14.3%
o 23086
14.3%
a 23086
14.3%
t 23086
14.3%
e 23086
14.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 138516
85.7%
Uppercase Letter 23086
 
14.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 46172
33.3%
o 23086
16.7%
a 23086
16.7%
t 23086
16.7%
e 23086
16.7%
Uppercase Letter
ValueCountFrequency (%)
D 23086
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 161602
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 46172
28.6%
D 23086
14.3%
o 23086
14.3%
a 23086
14.3%
t 23086
14.3%
e 23086
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 161602
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 46172
28.6%
D 23086
14.3%
o 23086
14.3%
a 23086
14.3%
t 23086
14.3%
e 23086
14.3%
Distinct11782
Distinct (%)51.0%
Missing0
Missing (%)0.0%
Memory size360.7 KiB
Minimum1932-10-11 00:00:00
Maximum2004-12-21 00:00:00
2023-09-04T18:36:02.932047image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:36:02.998845image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct365
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size360.7 KiB
Minimum2023-01-01 00:00:00
Maximum2023-12-31 00:00:00
2023-09-04T18:36:03.061652image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:36:03.124785image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Edad
Real number (ℝ)

Distinct73
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.314216
Minimum18
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size360.7 KiB
2023-09-04T18:36:03.194140image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile27
Q134
median40
Q348
95-th percentile67
Maximum90
Range72
Interquartile range (IQR)14

Descriptive statistics

Standard deviation11.981736
Coefficient of variation (CV)0.283161
Kurtosis1.1502825
Mean42.314216
Median Absolute Deviation (MAD)7
Skewness1.0093693
Sum976866
Variance143.562
MonotonicityNot monotonic
2023-09-04T18:36:03.258024image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42 1041
 
4.5%
41 988
 
4.3%
40 951
 
4.1%
37 949
 
4.1%
39 944
 
4.1%
38 930
 
4.0%
43 928
 
4.0%
36 885
 
3.8%
34 839
 
3.6%
33 838
 
3.6%
Other values (63) 13793
59.7%
ValueCountFrequency (%)
18 45
 
0.2%
19 72
 
0.3%
20 35
 
0.2%
21 47
 
0.2%
22 69
 
0.3%
23 117
 
0.5%
24 228
1.0%
25 209
0.9%
26 256
1.1%
27 402
1.7%
ValueCountFrequency (%)
90 5
 
< 0.1%
89 7
 
< 0.1%
88 9
 
< 0.1%
87 16
0.1%
86 9
 
< 0.1%
85 32
0.1%
84 27
0.1%
83 23
0.1%
82 31
0.1%
81 33
0.1%
Distinct2803
Distinct (%)12.1%
Missing0
Missing (%)0.0%
Memory size360.7 KiB
Minimum2004-04-22 00:00:00
Maximum2023-03-02 00:00:00
2023-09-04T18:36:03.323099image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:36:03.384857image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Monto Actual
Real number (ℝ)

SKEWED 

Distinct91
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36711.199
Minimum1667
Maximum4500000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size360.7 KiB
2023-09-04T18:36:03.454232image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1667
5-th percentile20000
Q130000
median30000
Q350000
95-th percentile60000
Maximum4500000
Range4498333
Interquartile range (IQR)20000

Descriptive statistics

Standard deviation42788.34
Coefficient of variation (CV)1.1655392
Kurtosis5420.091
Mean36711.199
Median Absolute Deviation (MAD)10000
Skewness58.931838
Sum8.4751474 × 108
Variance1.8308421 × 109
MonotonicityNot monotonic
2023-09-04T18:36:03.518238image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30000 9414
40.8%
50000 5327
23.1%
20000 4061
17.6%
25000 856
 
3.7%
40000 760
 
3.3%
35000 642
 
2.8%
60000 527
 
2.3%
15000 286
 
1.2%
100000 282
 
1.2%
10000 238
 
1.0%
Other values (81) 693
 
3.0%
ValueCountFrequency (%)
1667 1
 
< 0.1%
2000 1
 
< 0.1%
4000 1
 
< 0.1%
5000 8
 
< 0.1%
6667 4
 
< 0.1%
7000 1
 
< 0.1%
7500 2
 
< 0.1%
8333 2
 
< 0.1%
8334 1
 
< 0.1%
10000 238
1.0%
ValueCountFrequency (%)
4500000 1
 
< 0.1%
1650000 1
 
< 0.1%
1500000 2
 
< 0.1%
1300000 1
 
< 0.1%
1200000 1
 
< 0.1%
1000000 2
 
< 0.1%
650000 1
 
< 0.1%
520000 1
 
< 0.1%
500000 4
< 0.1%
400000 5
< 0.1%

Género
Categorical

MISSING 

Distinct3
Distinct (%)< 0.1%
Missing400
Missing (%)1.7%
Memory size203.2 KiB
Femenino
15127 
Masculino
7552 
Otro
 
7

Length

Max length9
Median length8
Mean length8.3316583
Min length4

Characters and Unicode

Total characters189012
Distinct characters15
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMasculino
2nd rowFemenino
3rd rowMasculino
4th rowFemenino
5th rowFemenino

Common Values

ValueCountFrequency (%)
Femenino 15127
65.5%
Masculino 7552
32.7%
Otro 7
 
< 0.1%
(Missing) 400
 
1.7%

Length

2023-09-04T18:36:03.576361image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-04T18:36:03.628908image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
femenino 15127
66.7%
masculino 7552
33.3%
otro 7
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
n 37806
20.0%
e 30254
16.0%
o 22686
12.0%
i 22679
12.0%
F 15127
8.0%
m 15127
8.0%
M 7552
 
4.0%
a 7552
 
4.0%
s 7552
 
4.0%
c 7552
 
4.0%
Other values (5) 15125
8.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 166326
88.0%
Uppercase Letter 22686
 
12.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 37806
22.7%
e 30254
18.2%
o 22686
13.6%
i 22679
13.6%
m 15127
9.1%
a 7552
 
4.5%
s 7552
 
4.5%
c 7552
 
4.5%
u 7552
 
4.5%
l 7552
 
4.5%
Other values (2) 14
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
F 15127
66.7%
M 7552
33.3%
O 7
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 189012
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 37806
20.0%
e 30254
16.0%
o 22686
12.0%
i 22679
12.0%
F 15127
8.0%
m 15127
8.0%
M 7552
 
4.0%
a 7552
 
4.0%
s 7552
 
4.0%
c 7552
 
4.0%
Other values (5) 15125
8.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 189012
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 37806
20.0%
e 30254
16.0%
o 22686
12.0%
i 22679
12.0%
F 15127
8.0%
m 15127
8.0%
M 7552
 
4.0%
a 7552
 
4.0%
s 7552
 
4.0%
c 7552
 
4.0%
Other values (5) 15125
8.0%

Estado Civil
Categorical

MISSING 

Distinct7
Distinct (%)< 0.1%
Missing2428
Missing (%)10.5%
Memory size203.6 KiB
Casado
9550 
Soltero
6572 
Concubinato
3382 
Divorciado
 
459
Separado
 
440
Other values (2)
 
255

Length

Max length11
Median length10
Mean length7.2567044
Min length5

Characters and Unicode

Total characters149909
Distinct characters28
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCasado
2nd rowCasado
3rd rowCasado
4th rowSoltero
5th rowCasado

Common Values

ValueCountFrequency (%)
Casado 9550
41.4%
Soltero 6572
28.5%
Concubinato 3382
 
14.6%
Divorciado 459
 
2.0%
Separado 440
 
1.9%
Viudo 252
 
1.1%
UNION LIBRE 3
 
< 0.1%
(Missing) 2428
 
10.5%

Length

2023-09-04T18:36:03.677880image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-04T18:36:03.744681image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
casado 9550
46.2%
soltero 6572
31.8%
concubinato 3382
 
16.4%
divorciado 459
 
2.2%
separado 440
 
2.1%
viudo 252
 
1.2%
union 3
 
< 0.1%
libre 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
o 31068
20.7%
a 23821
15.9%
C 12932
8.6%
d 10701
 
7.1%
t 9954
 
6.6%
s 9550
 
6.4%
r 7471
 
5.0%
S 7012
 
4.7%
e 7012
 
4.7%
n 6764
 
4.5%
Other values (18) 23624
15.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 129221
86.2%
Uppercase Letter 20685
 
13.8%
Space Separator 3
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 31068
24.0%
a 23821
18.4%
d 10701
 
8.3%
t 9954
 
7.7%
s 9550
 
7.4%
r 7471
 
5.8%
e 7012
 
5.4%
n 6764
 
5.2%
l 6572
 
5.1%
i 4552
 
3.5%
Other values (5) 11756
 
9.1%
Uppercase Letter
ValueCountFrequency (%)
C 12932
62.5%
S 7012
33.9%
D 459
 
2.2%
V 252
 
1.2%
N 6
 
< 0.1%
I 6
 
< 0.1%
U 3
 
< 0.1%
O 3
 
< 0.1%
L 3
 
< 0.1%
B 3
 
< 0.1%
Other values (2) 6
 
< 0.1%
Space Separator
ValueCountFrequency (%)
3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 149906
> 99.9%
Common 3
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 31068
20.7%
a 23821
15.9%
C 12932
8.6%
d 10701
 
7.1%
t 9954
 
6.6%
s 9550
 
6.4%
r 7471
 
5.0%
S 7012
 
4.7%
e 7012
 
4.7%
n 6764
 
4.5%
Other values (17) 23621
15.8%
Common
ValueCountFrequency (%)
3
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 149909
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 31068
20.7%
a 23821
15.9%
C 12932
8.6%
d 10701
 
7.1%
t 9954
 
6.6%
s 9550
 
6.4%
r 7471
 
5.0%
S 7012
 
4.7%
e 7012
 
4.7%
n 6764
 
4.5%
Other values (18) 23624
15.8%

Cantidad de Hijos
Real number (ℝ)

ZEROS 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1460625
Minimum0
Maximum10
Zeros7285
Zeros (%)31.6%
Negative0
Negative (%)0.0%
Memory size360.7 KiB
2023-09-04T18:36:03.801295image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile3
Maximum10
Range10
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.0341259
Coefficient of variation (CV)0.90232937
Kurtosis1.370252
Mean1.1460625
Median Absolute Deviation (MAD)1
Skewness0.87600025
Sum26458
Variance1.0694164
MonotonicityNot monotonic
2023-09-04T18:36:03.860591image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1 7798
33.8%
0 7285
31.6%
2 6039
26.2%
3 1472
 
6.4%
4 343
 
1.5%
5 117
 
0.5%
6 22
 
0.1%
7 6
 
< 0.1%
8 2
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
0 7285
31.6%
1 7798
33.8%
2 6039
26.2%
3 1472
 
6.4%
4 343
 
1.5%
5 117
 
0.5%
6 22
 
0.1%
7 6
 
< 0.1%
8 2
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
10 1
 
< 0.1%
9 1
 
< 0.1%
8 2
 
< 0.1%
7 6
 
< 0.1%
6 22
 
0.1%
5 117
 
0.5%
4 343
 
1.5%
3 1472
 
6.4%
2 6039
26.2%
1 7798
33.8%

Tiene hijos
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size203.1 KiB
Si
15801 
No
7285 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters46172
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowSi
3rd rowSi
4th rowNo
5th rowSi

Common Values

ValueCountFrequency (%)
Si 15801
68.4%
No 7285
31.6%

Length

2023-09-04T18:36:03.935382image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-04T18:36:03.984209image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
si 15801
68.4%
no 7285
31.6%

Most occurring characters

ValueCountFrequency (%)
S 15801
34.2%
i 15801
34.2%
N 7285
15.8%
o 7285
15.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 23086
50.0%
Lowercase Letter 23086
50.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 15801
68.4%
N 7285
31.6%
Lowercase Letter
ValueCountFrequency (%)
i 15801
68.4%
o 7285
31.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 46172
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 15801
34.2%
i 15801
34.2%
N 7285
15.8%
o 7285
15.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 46172
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 15801
34.2%
i 15801
34.2%
N 7285
15.8%
o 7285
15.8%

Ocupación
Text

MISSING 

Distinct145
Distinct (%)0.8%
Missing4672
Missing (%)20.2%
Memory size1.6 MiB
2023-09-04T18:36:04.113998image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length50
Median length39
Mean length10.30113
Min length4

Characters and Unicode

Total characters189685
Distinct characters72
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27 ?
Unique (%)0.1%

Sample

1st rowPolitico
2nd rowJubilado
3rd rowIngeniero
4th rowEmpleado
5th rowAbogado
ValueCountFrequency (%)
empleado 2980
 
13.6%
ingeniero 2882
 
13.1%
administrador 1484
 
6.8%
profesor 958
 
4.4%
abogado 938
 
4.3%
de 900
 
4.1%
contador 801
 
3.6%
otra 740
 
3.4%
trabajador 675
 
3.1%
independiente 675
 
3.1%
Other values (164) 8919
40.6%
2023-09-04T18:36:04.336001image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 21003
 
11.1%
e 18425
 
9.7%
a 16310
 
8.6%
r 15251
 
8.0%
i 14363
 
7.6%
n 13563
 
7.2%
d 13363
 
7.0%
t 8180
 
4.3%
s 7485
 
3.9%
m 6840
 
3.6%
Other values (62) 54902
28.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 163625
86.3%
Uppercase Letter 21935
 
11.6%
Space Separator 3538
 
1.9%
Other Punctuation 542
 
0.3%
Control 43
 
< 0.1%
Open Punctuation 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 21003
12.8%
e 18425
11.3%
a 16310
10.0%
r 15251
9.3%
i 14363
8.8%
n 13563
8.3%
d 13363
8.2%
t 8180
 
5.0%
s 7485
 
4.6%
m 6840
 
4.2%
Other values (21) 28842
17.6%
Uppercase Letter
ValueCountFrequency (%)
E 4236
19.3%
A 4119
18.8%
I 3769
17.2%
C 2320
10.6%
P 2117
9.7%
O 1069
 
4.9%
T 1048
 
4.8%
M 1014
 
4.6%
S 507
 
2.3%
D 460
 
2.1%
Other values (19) 1276
 
5.8%
Control
ValueCountFrequency (%)
“ 28
65.1%
‘ 9
 
20.9%
 3
 
7.0%
‰ 1
 
2.3%
š 1
 
2.3%
 1
 
2.3%
Other Punctuation
ValueCountFrequency (%)
/ 519
95.8%
, 16
 
3.0%
? 7
 
1.3%
Space Separator
ValueCountFrequency (%)
3538
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 185560
97.8%
Common 4125
 
2.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 21003
11.3%
e 18425
 
9.9%
a 16310
 
8.8%
r 15251
 
8.2%
i 14363
 
7.7%
n 13563
 
7.3%
d 13363
 
7.2%
t 8180
 
4.4%
s 7485
 
4.0%
m 6840
 
3.7%
Other values (50) 50777
27.4%
Common
ValueCountFrequency (%)
3538
85.8%
/ 519
 
12.6%
“ 28
 
0.7%
, 16
 
0.4%
‘ 9
 
0.2%
? 7
 
0.2%
 3
 
0.1%
‰ 1
 
< 0.1%
š 1
 
< 0.1%
( 1
 
< 0.1%
Other values (2) 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 187231
98.7%
None 2454
 
1.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 21003
11.2%
e 18425
 
9.8%
a 16310
 
8.7%
r 15251
 
8.1%
i 14363
 
7.7%
n 13563
 
7.2%
d 13363
 
7.1%
t 8180
 
4.4%
s 7485
 
4.0%
m 6840
 
3.7%
Other values (41) 52448
28.0%
None
ValueCountFrequency (%)
ó 1190
48.5%
é 624
25.4%
ñ 333
 
13.6%
í 127
 
5.2%
á 46
 
1.9%
ú 34
 
1.4%
Ó 32
 
1.3%
“ 28
 
1.1%
Ñ 12
 
0.5%
‘ 9
 
0.4%
Other values (11) 19
 
0.8%

Churn Probability
Real number (ℝ)

Distinct591
Distinct (%)2.6%
Missing187
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean0.36452006
Minimum0.0101
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size360.7 KiB
2023-09-04T18:36:04.420223image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.0101
5-th percentile0.0117
Q10.0334
median0.34
Q30.57
95-th percentile0.9
Maximum1
Range0.9899
Interquartile range (IQR)0.5366

Descriptive statistics

Standard deviation0.28958267
Coefficient of variation (CV)0.79442177
Kurtosis-0.84563526
Mean0.36452006
Median Absolute Deviation (MAD)0.2732
Skewness0.43727731
Sum8347.1448
Variance0.083858122
MonotonicityNot monotonic
2023-09-04T18:36:04.489610image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.33 518
 
2.2%
0.32 513
 
2.2%
0.34 511
 
2.2%
0.36 494
 
2.1%
0.37 488
 
2.1%
0.35 485
 
2.1%
0.31 476
 
2.1%
0.3 457
 
2.0%
0.38 415
 
1.8%
0.29 392
 
1.7%
Other values (581) 18150
78.6%
ValueCountFrequency (%)
0.0101 78
0.3%
0.0102 81
0.4%
0.0103 88
0.4%
0.0104 91
0.4%
0.0105 72
0.3%
0.0106 69
0.3%
0.0107 72
0.3%
0.0108 56
0.2%
0.0109 56
0.2%
0.011 61
0.3%
ValueCountFrequency (%)
1 79
0.3%
0.99 87
0.4%
0.98 101
0.4%
0.97 108
0.5%
0.96 102
0.4%
0.95 106
0.5%
0.94 109
0.5%
0.93 116
0.5%
0.92 107
0.5%
0.91 124
0.5%

Lapsed Probability
Real number (ℝ)

ZEROS 

Distinct2126
Distinct (%)9.3%
Missing187
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean0.063310699
Minimum0
Maximum0.8815
Zeros20060
Zeros (%)86.9%
Negative0
Negative (%)0.0%
Memory size360.7 KiB
2023-09-04T18:36:04.556508image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.55651
Maximum0.8815
Range0.8815
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.17448728
Coefficient of variation (CV)2.7560473
Kurtosis5.0765881
Mean0.063310699
Median Absolute Deviation (MAD)0
Skewness2.5684029
Sum1449.7517
Variance0.03044581
MonotonicityNot monotonic
2023-09-04T18:36:04.717991image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 20060
86.9%
0.5715 6
 
< 0.1%
0.5402 6
 
< 0.1%
0.5973 5
 
< 0.1%
0.5987 5
 
< 0.1%
0.5824 5
 
< 0.1%
0.5015 4
 
< 0.1%
0.6087 4
 
< 0.1%
0.558 4
 
< 0.1%
0.5717 4
 
< 0.1%
Other values (2116) 2796
 
12.1%
(Missing) 187
 
0.8%
ValueCountFrequency (%)
0 20060
86.9%
0.089 1
 
< 0.1%
0.09 1
 
< 0.1%
0.0907 1
 
< 0.1%
0.0919 1
 
< 0.1%
0.0993 1
 
< 0.1%
0.1039 1
 
< 0.1%
0.1057 1
 
< 0.1%
0.1122 1
 
< 0.1%
0.1192 1
 
< 0.1%
ValueCountFrequency (%)
0.8815 1
< 0.1%
0.8793 1
< 0.1%
0.8762 1
< 0.1%
0.8691 1
< 0.1%
0.8685 1
< 0.1%
0.8661 1
< 0.1%
0.8651 1
< 0.1%
0.8616 1
< 0.1%
0.8591 1
< 0.1%
0.8561 1
< 0.1%

RFM Segmento Actual
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing213
Missing (%)0.9%
Memory size2.8 MiB
Otra Clasificación
22873 

Length

Max length18
Median length18
Mean length18
Min length18

Characters and Unicode

Total characters411714
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOtra Clasificación
2nd rowOtra Clasificación
3rd rowOtra Clasificación
4th rowOtra Clasificación
5th rowOtra Clasificación

Common Values

ValueCountFrequency (%)
Otra Clasificación 22873
99.1%
(Missing) 213
 
0.9%

Length

2023-09-04T18:36:04.774023image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-04T18:36:04.822137image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
otra 22873
50.0%
clasificación 22873
50.0%

Most occurring characters

ValueCountFrequency (%)
a 68619
16.7%
i 68619
16.7%
c 45746
11.1%
O 22873
 
5.6%
t 22873
 
5.6%
r 22873
 
5.6%
22873
 
5.6%
C 22873
 
5.6%
l 22873
 
5.6%
s 22873
 
5.6%
Other values (3) 68619
16.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 343095
83.3%
Uppercase Letter 45746
 
11.1%
Space Separator 22873
 
5.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 68619
20.0%
i 68619
20.0%
c 45746
13.3%
t 22873
 
6.7%
r 22873
 
6.7%
l 22873
 
6.7%
s 22873
 
6.7%
f 22873
 
6.7%
ó 22873
 
6.7%
n 22873
 
6.7%
Uppercase Letter
ValueCountFrequency (%)
O 22873
50.0%
C 22873
50.0%
Space Separator
ValueCountFrequency (%)
22873
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 388841
94.4%
Common 22873
 
5.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 68619
17.6%
i 68619
17.6%
c 45746
11.8%
O 22873
 
5.9%
t 22873
 
5.9%
r 22873
 
5.9%
C 22873
 
5.9%
l 22873
 
5.9%
s 22873
 
5.9%
f 22873
 
5.9%
Other values (2) 45746
11.8%
Common
ValueCountFrequency (%)
22873
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 388841
94.4%
None 22873
 
5.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 68619
17.6%
i 68619
17.6%
c 45746
11.8%
O 22873
 
5.9%
t 22873
 
5.9%
r 22873
 
5.9%
22873
 
5.9%
C 22873
 
5.9%
l 22873
 
5.9%
s 22873
 
5.9%
Other values (2) 45746
11.8%
None
ValueCountFrequency (%)
ó 22873
100.0%

Otra Clasificación RFM Actual
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing213
Missing (%)0.9%
Memory size1.6 MiB
Constantes
19457 
Ideales
2699 
Distantes
 
627
Extraviados
 
90

Length

Max length11
Median length10
Mean length9.6225244
Min length7

Characters and Unicode

Total characters220096
Distinct characters16
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowConstantes
2nd rowConstantes
3rd rowConstantes
4th rowConstantes
5th rowConstantes

Common Values

ValueCountFrequency (%)
Constantes 19457
84.3%
Ideales 2699
 
11.7%
Distantes 627
 
2.7%
Extraviados 90
 
0.4%
(Missing) 213
 
0.9%

Length

2023-09-04T18:36:04.867625image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-04T18:36:04.926479image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
constantes 19457
85.1%
ideales 2699
 
11.8%
distantes 627
 
2.7%
extraviados 90
 
0.4%

Most occurring characters

ValueCountFrequency (%)
s 42957
19.5%
t 40258
18.3%
n 39541
18.0%
e 25482
11.6%
a 22963
10.4%
o 19547
8.9%
C 19457
8.8%
d 2789
 
1.3%
I 2699
 
1.2%
l 2699
 
1.2%
Other values (6) 1704
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 197223
89.6%
Uppercase Letter 22873
 
10.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 42957
21.8%
t 40258
20.4%
n 39541
20.0%
e 25482
12.9%
a 22963
11.6%
o 19547
9.9%
d 2789
 
1.4%
l 2699
 
1.4%
i 717
 
0.4%
x 90
 
< 0.1%
Other values (2) 180
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
C 19457
85.1%
I 2699
 
11.8%
D 627
 
2.7%
E 90
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 220096
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 42957
19.5%
t 40258
18.3%
n 39541
18.0%
e 25482
11.6%
a 22963
10.4%
o 19547
8.9%
C 19457
8.8%
d 2789
 
1.3%
I 2699
 
1.2%
l 2699
 
1.2%
Other values (6) 1704
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 220096
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 42957
19.5%
t 40258
18.3%
n 39541
18.0%
e 25482
11.6%
a 22963
10.4%
o 19547
8.9%
C 19457
8.8%
d 2789
 
1.3%
I 2699
 
1.2%
l 2699
 
1.2%
Other values (6) 1704
 
0.8%

Cantidad Cuotas Pagadas Global
Real number (ℝ)

HIGH CORRELATION 

Distinct260
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.979338
Minimum0
Maximum358
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size360.7 KiB
2023-09-04T18:36:04.982447image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q113
median29
Q367
95-th percentile121
Maximum358
Range358
Interquartile range (IQR)54

Descriptive statistics

Standard deviation43.19535
Coefficient of variation (CV)0.96033762
Kurtosis4.8282524
Mean44.979338
Median Absolute Deviation (MAD)20
Skewness1.9005648
Sum1038393
Variance1865.8383
MonotonicityNot monotonic
2023-09-04T18:36:05.045732image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 628
 
2.7%
9 626
 
2.7%
12 610
 
2.6%
10 588
 
2.5%
8 585
 
2.5%
11 563
 
2.4%
14 526
 
2.3%
13 509
 
2.2%
7 504
 
2.2%
15 499
 
2.2%
Other values (250) 17448
75.6%
ValueCountFrequency (%)
0 3
 
< 0.1%
1 146
 
0.6%
2 175
 
0.8%
3 242
 
1.0%
4 291
1.3%
5 479
2.1%
6 628
2.7%
7 504
2.2%
8 585
2.5%
9 626
2.7%
ValueCountFrequency (%)
358 1
< 0.1%
340 1
< 0.1%
336 1
< 0.1%
320 1
< 0.1%
318 1
< 0.1%
317 1
< 0.1%
316 2
< 0.1%
312 2
< 0.1%
310 1
< 0.1%
308 2
< 0.1%

Cantidad Cuotas No Pagadas Global
Real number (ℝ)

ZEROS 

Distinct99
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.4666898
Minimum0
Maximum118
Zeros9269
Zeros (%)40.1%
Negative0
Negative (%)0.0%
Memory size360.7 KiB
2023-09-04T18:36:05.111064image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q38
95-th percentile30
Maximum118
Range118
Interquartile range (IQR)8

Descriptive statistics

Standard deviation11.326489
Coefficient of variation (CV)1.7515127
Kurtosis11.845602
Mean6.4666898
Median Absolute Deviation (MAD)2
Skewness3.012671
Sum149290
Variance128.28936
MonotonicityNot monotonic
2023-09-04T18:36:05.178009image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9269
40.1%
1 2257
 
9.8%
2 1337
 
5.8%
4 1184
 
5.1%
3 1102
 
4.8%
5 856
 
3.7%
6 652
 
2.8%
8 490
 
2.1%
7 486
 
2.1%
12 404
 
1.7%
Other values (89) 5049
21.9%
ValueCountFrequency (%)
0 9269
40.1%
1 2257
 
9.8%
2 1337
 
5.8%
3 1102
 
4.8%
4 1184
 
5.1%
5 856
 
3.7%
6 652
 
2.8%
7 486
 
2.1%
8 490
 
2.1%
9 390
 
1.7%
ValueCountFrequency (%)
118 1
< 0.1%
114 1
< 0.1%
107 1
< 0.1%
105 1
< 0.1%
101 1
< 0.1%
100 1
< 0.1%
99 1
< 0.1%
96 1
< 0.1%
95 1
< 0.1%
94 1
< 0.1%
Distinct98
Distinct (%)0.4%
Missing122
Missing (%)0.5%
Memory size1.8 MiB
2023-09-04T18:36:05.295744image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length46
Median length44
Mean length15.218603
Min length6

Characters and Unicode

Total characters349480
Distinct characters66
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)0.1%

Sample

1st rowCOF2FR.F. 2021
2nd rowCOWCB R.F.2022
3rd rowSPIF F2F 2016
4th rowCOF2FR.F.2022
5th rowCOWCB R.F.2022
ValueCountFrequency (%)
f2f 7917
 
14.0%
cof2fr.f.2022 3926
 
6.9%
cowcb 3817
 
6.8%
spif 2864
 
5.1%
2019 2751
 
4.9%
2018 2588
 
4.6%
para 2486
 
4.4%
2021 2451
 
4.3%
cof2fr.f 2430
 
4.3%
r.f.2022 2070
 
3.7%
Other values (120) 23217
41.1%
2023-09-04T18:36:05.502658image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 55644
15.9%
F 46127
13.2%
33762
 
9.7%
0 23436
 
6.7%
. 22692
 
6.5%
R 19166
 
5.5%
C 18528
 
5.3%
I 15602
 
4.5%
O 14362
 
4.1%
1 13748
 
3.9%
Other values (56) 86413
24.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 174100
49.8%
Decimal Number 105177
30.1%
Space Separator 33762
 
9.7%
Other Punctuation 23174
 
6.6%
Lowercase Letter 9838
 
2.8%
Dash Punctuation 2346
 
0.7%
Connector Punctuation 519
 
0.1%
Close Punctuation 282
 
0.1%
Open Punctuation 282
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 1999
20.3%
s 1075
10.9%
e 1075
10.9%
a 757
 
7.7%
d 637
 
6.5%
c 615
 
6.3%
b 587
 
6.0%
u 482
 
4.9%
k 382
 
3.9%
i 369
 
3.8%
Other values (15) 1860
18.9%
Uppercase Letter
ValueCountFrequency (%)
F 46127
26.5%
R 19166
11.0%
C 18528
10.6%
I 15602
 
9.0%
O 14362
 
8.2%
S 8842
 
5.1%
A 8200
 
4.7%
P 6766
 
3.9%
E 5987
 
3.4%
B 5145
 
3.0%
Other values (14) 25375
14.6%
Decimal Number
ValueCountFrequency (%)
2 55644
52.9%
0 23436
22.3%
1 13748
 
13.1%
9 2759
 
2.6%
8 2588
 
2.5%
6 1975
 
1.9%
3 1672
 
1.6%
7 1333
 
1.3%
4 1133
 
1.1%
5 889
 
0.8%
Other Punctuation
ValueCountFrequency (%)
. 22692
97.9%
# 482
 
2.1%
Space Separator
ValueCountFrequency (%)
33762
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2346
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 519
100.0%
Close Punctuation
ValueCountFrequency (%)
) 282
100.0%
Open Punctuation
ValueCountFrequency (%)
( 282
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 183938
52.6%
Common 165542
47.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
F 46127
25.1%
R 19166
10.4%
C 18528
10.1%
I 15602
 
8.5%
O 14362
 
7.8%
S 8842
 
4.8%
A 8200
 
4.5%
P 6766
 
3.7%
E 5987
 
3.3%
B 5145
 
2.8%
Other values (39) 35213
19.1%
Common
ValueCountFrequency (%)
2 55644
33.6%
33762
20.4%
0 23436
14.2%
. 22692
13.7%
1 13748
 
8.3%
9 2759
 
1.7%
8 2588
 
1.6%
- 2346
 
1.4%
6 1975
 
1.2%
3 1672
 
1.0%
Other values (7) 4920
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 348368
99.7%
None 1112
 
0.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 55644
16.0%
F 46127
13.2%
33762
 
9.7%
0 23436
 
6.7%
. 22692
 
6.5%
R 19166
 
5.5%
C 18528
 
5.3%
I 15602
 
4.5%
O 14362
 
4.1%
1 13748
 
3.9%
Other values (53) 85301
24.5%
None
ValueCountFrequency (%)
Ó 1052
94.6%
ó 59
 
5.3%
á 1
 
0.1%

Donante Activo
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
1
23086 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23086
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 23086
100.0%

Length

2023-09-04T18:36:05.576681image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-04T18:36:05.623351image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 23086
100.0%

Most occurring characters

ValueCountFrequency (%)
1 23086
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23086
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 23086
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 23086
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 23086
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23086
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 23086
100.0%

Edad_donacion
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct19
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1894222
Minimum0
Maximum19
Zeros939
Zeros (%)4.1%
Negative0
Negative (%)0.0%
Memory size270.5 KiB
2023-09-04T18:36:05.663634image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median3
Q37
95-th percentile13
Maximum19
Range19
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.4878213
Coefficient of variation (CV)0.8325304
Kurtosis0.54564948
Mean4.1894222
Median Absolute Deviation (MAD)2
Skewness1.0381354
Sum96717
Variance12.164897
MonotonicityNot monotonic
2023-09-04T18:36:05.715478image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
1 5909
25.6%
2 3895
16.9%
4 2178
 
9.4%
7 1918
 
8.3%
8 1734
 
7.5%
6 1601
 
6.9%
5 1399
 
6.1%
3 1216
 
5.3%
13 1175
 
5.1%
0 939
 
4.1%
Other values (9) 1122
 
4.9%
ValueCountFrequency (%)
0 939
 
4.1%
1 5909
25.6%
2 3895
16.9%
3 1216
 
5.3%
4 2178
 
9.4%
5 1399
 
6.1%
6 1601
 
6.9%
7 1918
 
8.3%
8 1734
 
7.5%
9 615
 
2.7%
ValueCountFrequency (%)
19 26
 
0.1%
18 15
 
0.1%
17 11
 
< 0.1%
16 3
 
< 0.1%
15 3
 
< 0.1%
13 1175
5.1%
12 146
 
0.6%
11 123
 
0.5%
10 180
 
0.8%
9 615
2.7%

Interactions

2023-09-04T18:36:01.793588image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:35:58.320275image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:35:58.965637image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:35:59.405313image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:35:59.854228image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:36:00.393099image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:36:00.841881image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:36:01.325376image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:36:01.853828image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:35:58.520984image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:35:59.022428image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:35:59.463209image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:35:59.917147image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:36:00.450642image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:36:00.903058image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:36:01.386320image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:36:01.908951image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:35:58.618794image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:35:59.073283image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:35:59.516507image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:35:59.970960image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:36:00.505265image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:36:00.959033image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:36:01.442544image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:36:01.966674image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:35:58.674256image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:35:59.126682image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:35:59.570821image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:36:00.030014image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:36:00.559069image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:36:01.017244image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:36:01.498869image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:36:02.024736image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:35:58.731616image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:35:59.182041image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:35:59.625316image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:36:00.086077image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:36:00.613223image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:36:01.079702image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:36:01.555504image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:36:02.088681image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:35:58.786003image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:35:59.234896image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:35:59.679155image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:36:00.212589image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:36:00.668340image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:36:01.136791image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:36:01.614091image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:36:02.150435image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:35:58.846082image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:35:59.291885image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:35:59.738048image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:36:00.272043image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:36:00.727685image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:36:01.199193image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:36:01.674537image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:36:02.212363image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:35:58.905757image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:35:59.348258image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:35:59.795966image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:36:00.330429image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:36:00.784379image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:36:01.262265image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-04T18:36:01.732949image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-09-04T18:36:05.772297image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
EdadMonto ActualCantidad de HijosChurn ProbabilityLapsed ProbabilityCantidad Cuotas Pagadas GlobalCantidad Cuotas No Pagadas GlobalEdad_donacionGéneroEstado CivilTiene hijosOtra Clasificación RFM Actual
Edad1.0000.0530.2790.102-0.1100.3850.1130.3710.0660.2100.2860.173
Monto Actual0.0531.0000.0060.001-0.020-0.080-0.133-0.1440.0000.0160.0070.037
Cantidad de Hijos0.2790.0061.0000.0690.010-0.075-0.048-0.0890.0230.1520.4940.028
Churn Probability0.1020.0010.0691.000-0.3120.070-0.249-0.0020.1170.1050.1180.292
Lapsed Probability-0.110-0.0200.010-0.3121.000-0.2460.291-0.1320.0170.0340.0310.141
Cantidad Cuotas Pagadas Global0.385-0.080-0.0750.070-0.2461.0000.2880.9290.0800.0780.2120.338
Cantidad Cuotas No Pagadas Global0.113-0.133-0.048-0.2490.2910.2881.0000.4780.0540.0230.1250.133
Edad_donacion0.371-0.144-0.089-0.002-0.1320.9290.4781.0000.0830.0770.2540.322
Género0.0660.0000.0230.1170.0170.0800.0540.0831.0000.0920.0210.062
Estado Civil0.2100.0160.1520.1050.0340.0780.0230.0770.0921.0000.4850.079
Tiene hijos0.2860.0070.4940.1180.0310.2120.1250.2540.0210.4851.0000.112
Otra Clasificación RFM Actual0.1730.0370.0280.2920.1410.3380.1330.3220.0620.0790.1121.000

Missing values

2023-09-04T18:36:02.399175image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-09-04T18:36:02.567380image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-09-04T18:36:02.728360image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Tipo de registro del contactoFecha de nacimientoFecha Aniversario PagoEdadFecha de CaptaciónMonto ActualGéneroEstado CivilCantidad de HijosTiene hijosOcupaciónChurn ProbabilityLapsed ProbabilityRFM Segmento ActualOtra Clasificación RFM ActualCantidad Cuotas Pagadas GlobalCantidad Cuotas No Pagadas GlobalCampaña Inicial: NombreDonante ActivoEdad_donacion
PSN
1020456818Donante1964-06-162023-07-0159.02021-06-1930000.0MasculinoCasado0.0NoNaN0.59000.000Otra ClasificaciónConstantes260COF2FR.F. 202112
1020483942Donante1975-08-282023-10-0147.02022-10-0150000.0FemeninoCasado1.0SiPolitico0.78000.000Otra ClasificaciónConstantes100COWCB R.F.202211
1020276927Donante1941-09-232023-01-3181.02015-12-2920000.0MasculinoCasado2.0SiJubilado0.53000.000Otra ClasificaciónConstantes8110SPIF F2F 201618
1020476173Donante1988-10-282023-04-1834.02022-04-1730000.0FemeninoSoltero0.0NoIngeniero0.95000.000Otra ClasificaciónConstantes160COF2FR.F.202211
1020483943Donante1982-04-222023-10-0141.02022-10-0130000.0FemeninoCasado2.0SiEmpleado0.92000.000Otra ClasificaciónConstantes100COWCB R.F.202211
1020276943Donante1985-03-042023-01-3138.02015-12-2920000.0FemeninoConcubinato2.0SiNaN0.01040.581Otra ClasificaciónConstantes3759SPIF F2F 201618
1020490301Donante1985-04-302023-03-0138.02023-02-1830000.0FemeninoSoltero2.0SiNaN0.01090.000Otra ClasificaciónConstantes50COF2FR.F.202310
1020483945Donante1994-04-172023-12-0129.02022-10-0130000.0FemeninoCasado1.0SiAbogado0.02990.000Otra ClasificaciónDistantes63COWCB R.F.202211
1020490303Donante1967-02-022023-04-0156.02023-02-1830000.0FemeninoSoltero0.0NoPsicólogo0.01660.000NaNNaN40COF2FR.F.202310
1020476245Donante1988-08-092023-05-0134.02022-04-1730000.0MasculinoSoltero1.0SiDiseñador0.87000.000Otra ClasificaciónConstantes150COF2FR.F.202211
Tipo de registro del contactoFecha de nacimientoFecha Aniversario PagoEdadFecha de CaptaciónMonto ActualGéneroEstado CivilCantidad de HijosTiene hijosOcupaciónChurn ProbabilityLapsed ProbabilityRFM Segmento ActualOtra Clasificación RFM ActualCantidad Cuotas Pagadas GlobalCantidad Cuotas No Pagadas GlobalCampaña Inicial: NombreDonante ActivoEdad_donacion
PSN
1020436623Donante1987-01-182023-10-0136.02018-09-1430000.0FemeninoSoltero1.0SiIngeniero0.420.0Otra ClasificaciónConstantes590F2F RE-INVERSIÓN 201815
1020436631Donante1974-07-302023-12-2049.02018-09-2330000.0FemeninoSoltero1.0SiIngeniero0.390.0Otra ClasificaciónConstantes4513IF4C F2F II 201815
1020436658Donante1990-12-262023-10-3132.02018-09-1950000.0FemeninoSoltero1.0SiIngeniero0.200.0Otra ClasificaciónConstantes576COWCB R.F.202215
1020436681Donante1966-05-192023-10-2557.02018-09-1825000.0MasculinoCasado2.0SiEmpleado0.270.0Otra ClasificaciónConstantes580F2F RE-INVERSIÓN 201815
1020436695Donante1961-01-012023-01-3062.02018-09-3050000.0FemeninoCasado2.0SiAbogado0.290.0Otra ClasificaciónConstantes508F2F REINV 2018 PARA 201915
1020436727Donante1978-06-272023-10-0545.02018-09-2950000.0MasculinoCasado2.0SiIngeniero0.300.0Otra ClasificaciónIdeales571IF4C F2F II 201815
1020438489Donante1987-06-192023-10-2536.02018-10-1350000.0MasculinoConcubinato1.0SiEmpleado0.300.0Otra ClasificaciónConstantes580F2F RE-INVERSIÓN 201815
1020438498Donante1971-08-192023-01-0251.02018-10-1360000.0MasculinoCasado2.0SiDiseñador0.220.0Otra ClasificaciónConstantes560F2F REINV 2018 PARA 201915
1020438513Donante1979-06-042023-02-1544.02018-10-1515000.0FemeninoSoltero1.0SiQuímico Farmacéutico0.370.0Otra ClasificaciónConstantes486F2F REINV 2018 PARA 201915
1020438536Donante1976-12-192023-11-0146.02018-10-1335000.0MasculinoConcubinato3.0SiPolicía/Militar0.260.0Otra ClasificaciónConstantes570IF4C F2F II 201815